Beyond Freshness: Closure-Aware Admission for Reliable Agent Memory

External memory enables large language model agents to accumulate and reuse information across long-running interactions and multi-step tasks. However, source updates, corrections, and retractions can invalidate derived summaries, plans, and cross-record conclusions. Such memories may violate task constraints even when individual updates are locally correct, records appear fresh, or different maintenance schedules converge to the same state. We study memory-use admission under evolving information: deciding which derived memories remain valid for a request under explicit dependencies and task constraints, while balancing correctness, service availability, and maintenance cost. Direct version checking can miss transitive invalidation; invalidation alone does not restore service; and global rejection and repeated full validation can introduce unnecessary blocking and redundant computation, respectively. We propose Closure-Aware Admission (CAA), which combines task-constraint validation with transitive dependency closure to quarantine inadmissible memories while preserving unaffected service. A persistent incremental implementation reuses validation results that remain valid after updates. Controlled language-model experiments expose the compositional failures motivating this task. Further evaluations across mechanism families, source-driven replay, and native execution on SQLite and Mem0 in raw-storage mode show that CAA matches the decisions of the full transitive-closure baseline under the evaluated contracts, avoiding unsafe admissions missed by direct version checks and unnecessary service losses caused by global rejection. Across the evaluated SQLite and Mem0 workloads, persistent CAA reduces aggregate online execution time relative to persistent full-closure validation while preserving the same admission decisions. These results demonstrate the benefits of reusing valid verification results for efficient and reliable memory maintenance.